Mathematical Modeling of Biofilm Eradication Using Optimal Control

TL;DR

This paper develops a mathematical model of bacterial biofilm resistance transfer and analyzes optimal antibiotic dosing strategies to effectively eradicate bacteria while minimizing total antibiotic use.

Contribution

It introduces a novel biofilm model incorporating horizontal gene transfer and derives optimal antibiotic dosing protocols using Pontryagin's maximum principle.

Findings

High doses are needed for bacterial elimination with continuous dosing.

Periodic dosing may fail if doses are insufficient.

Tapered dosing protocols are optimal and robust across parameters.

Abstract

We propose and analyze a model for antibiotic resistance transfer in a bacterial biofilm and examine antibiotic dosing strategies that are effective in bacterial elimination. In particular, we consider a 1-D model of a biofilm with susceptible, persistor and resistant bacteria. Resistance can be transferred to the susceptible bacteria via horizontal gene transfer (HGT), specifically via conjugation. We analyze some basic properties of the model, determine the conditions for existence of disinfection and coexistence states, including boundary equilibria and their stability. Numerical simulations are performed to explore different modeling scenarios and support our theoretical findings. Different antibiotic dosing strategies are then studied, starting with a continuous dosing; here we note that high doses of antibiotic are needed for bacterial elimination. We then consider periodic dosing, and again observe that insufficient levels of antibiotic per dose may lead to treatment failure. Finally, using an extended version of Pontryagin's maximum principle we determine efficient antibiotic dosing protocols, which ensure bacterial elimination while keeping the total dosing low; we note that this involves a tapered dosing which reinforces results presented in other clinical and modeling studies. We study the optimal dosing for different parameter values and note that the optimal dosing schedule is qualitatively robust.